Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass tutorials and applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. This project can also be found at iHub and Gitee.
🔥 3.0.0 has been pre-released, the current version supports TensorFlow, MindSpore and PaddlePaddle (partial) as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. In the future, it will support TensorFlow, MindSpore, PaddlePaddle, PyTorch and other backends.
🔥 Sipeed Maxi-EMC: Run TensorLayer models on the low-cost AI chip (e.g., K210) (Alpha Version)
TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic, making it easy to learn while being flexible enough to cope with complex AI tasks. TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University, Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in both English and Chinese.
If you want to try the experimental features on the the master branch, you can find the latest document here.
You can find a large collection of examples that use TensorLayer in here and the following space:
TensorLayer 2.0 relies on TensorFlow, numpy, and others. To use GPUs, CUDA and cuDNN are required.
pip3 install tensorflow-gpu==2.0.0-rc1 # TensorFlow GPU (version 2.0 RC1) pip3 install tensorflow # CPU version
Install the stable release of TensorLayer:
pip3 install tensorlayer
Install the unstable development version of TensorLayer:
pip3 install git+https://github.com/tensorlayer/tensorlayer.git
If you want to install the additional dependencies, you can also run
bash pip3 install --upgrade tensorlayer[all] # all additional dependencies pip3 install --upgrade tensorlayer[extra] # only the `extra` dependencies pip3 install --upgrade tensorlayer[contrib_loggers] # only the `contrib_loggers` dependencies
If you are TensorFlow 1.X users, you can use TensorLayer 1.11.0:
# For last stable version of TensorLayer 1.X pip3 install --upgrade tensorlayer==1.11.0